WP 3 - Modelling of spatial interaction between target species and fisheries including connectivity among Marine Managed Areas Russo T., D’Andrea L., Parisi A., Cataudella S. This project has been funded with support from the European Commission
WEB ADDRESS WP 3 - Modelling of spatial interaction between target species and fisheries including connectivity among Marine Managed Areas Lead: Conisma (Tommaso Russo) Participants: Conisma, CNR, OGS, IOF Duration: from month 6 to month 36 Objectives: To define a set of MMAs network scenarios based on different combinations of existing and new • MMAs To identify and evaluate the occurrence and magnitude of spillover effects (e.g. spawning products, • propagules, juveniles, adults) outside the network of marine protected areas in terms of stock abundance and fishery performance, considering prevailing hydrodynamics and the life cycles of the species; To understand the spatial structure of targeted fisheries with respect to the spatial distribution and • connectivity of the network(s) of marine protected areas; To evaluate the possible effects on the redistribution of fishing efforts, including small scale and • recreational fisheries; To evaluate, through a simulation approach, whether and how the establishment of no- trawling • zones would enhance the effectiveness and efficiency of the spatial-based approach to fisheries management towards achieving MSY objectives, considering also the socio economic effects; To evaluate whether, and the extent to which, human activities other than professional and • recreational fisheries may conceal or undermine the positive effects a network of marine protected areas may have on exploited biological resources and on fishing yields with respect to the MSY objectives.
WEB ADDRESS 6 3 To evaluate the extent to which To understand the spatial other human may conceal or structure of targeted fisheries undermine the positive effects with respect to the spatial of marine protected areas distribution and connectivity of marine protected areas 5 To evaluate, through a simulation approach, whether and how the 4 establishment of no- trawling zones would To evaluate the possible effects enhance the effectiveness and efficiency of on the redistribution of fishing the spatial-based approach to fisheries efforts management towards achieving MSY objectives, considering also the socio economic effects 2 1 To evaluate spillover effects To define a set of outside the network of marine MMAs network protected areas scenarios
WEB ADDRESS 6 3 To evaluate the extent to which To understand the spatial other human may conceal or structure of targeted fisheries undermine the positive effects with respect to the spatial of marine protected areas distribution and connectivity of marine protected areas 5 To evaluate, through a simulation approach, whether and how the 4 establishment of no- trawling zones would To evaluate the possible effects enhance the effectiveness and efficiency of on the redistribution of fishing the spatial-based approach to fisheries efforts management towards achieving MSY objectives, considering also the socio economic effects 2 1 To evaluate spillover effects To define a set of outside the network of marine MMAs network protected areas scenarios
WEB ADDRESS The path • All these objectives should be based on the cross- analysis of existing information about spatial behaviour of fleets (i.e. fishing effort and related catches), environmental drivers (i.e. connectivity) and biological dynamics of living resources. • The results of this cross- analysis will be integrated, to the extent possible, in stock assessment models to simulate the effect of area closure scenarios on the target stocks in ISIS-Fish (Pelletier et al., 2009) and SMART models (Russo et al., 2014).
WEB ADDRESS The path • Both ISIS-Fish and SMART are based on the partitioning the marine space (the “world” in which fleets operate and in which living resources live) into a set of areas with explicit reference to spatial and temporal structure of stocks and, accordingly, reflecting the main dynamics of exploitation (seasonal variability of fishing effort and of catches, even in terms of exploitation pattern and size spectra of catches). • Thus, the first step is represented by the identification of sub- areas (namely “Fishing grounds”) for each area of study. These fishing grounds will represent the basic units for the following analyses, including assessment of the connectivity and of the effect of different fishing effort patterns.
WEB ADDRESS Identification of fishing grounds: the case of the Strait of Sicily • The regionalisation procedure is carried out on a grid for each area (Adriatic and Strait of Sicily). • The provided grid comprises 6319 cells. • The input data is composed of: • bathymetry (downloaded by the National Oceanic and Atmospheric Administration - NOAA), • substrates (downloaded by the EMODNET Project website) and • the sum of the fishing time for each year for each cell of the grid.
WEB ADDRESS A 3x3 Km square grid was defined for the GSA 12, 13, 14, 15, and 16 (with the exception of the territorial waters of the North Africa)
WEB ADDRESS A 3x3 Km square grid was defined for the GSA 17 and 18
WEB ADDRESS Both SMART and ISIS-FISH assume that each system is “closed”
WEB ADDRESS Both SMART and ISIS-FISH assume that each system is “closed”
WEB ADDRESS Identification of fishing grounds: the case of the Strait of Sicily Substrates Bathymetry Effort CONSTRAINED CLUSTERING
WEB ADDRESS Identification of fishing grounds: the case of the Strait of Sicily The final output (preliminary evaluated by the CNR colleagues) comprises 50 fishing grounds
WEB ADDRESS Analysis of database of catches for the Red mullet in the Strait of Sicily After the identification of the sub areas (fishing grounds) for the Strait of • Sicily, a large database comprising the catches self-collected by the fishermen of a selected list of fishing vessels was processed. This database was kindly provided by the CNR IAMC as partner within the MANTIS Project. The dataset of the red mullet fishery from the Strait of Sicily is stored as a • data frame with the following fields: UTC, Length, Num of Fishing Ground, Year, Month. There are 22556 observations, one for each sampled fish. The length-frequency distribution of the population spans within a minimum • of 6 cm to a maximum of 26 cm, the mean value is about 17 cm. The sampling time-span ranges from the 2009 to the 2015, for a total of 7 • years of data. The sampling points positions are collected from 19 of the 50 fishing ground considered in this case study.
WEB ADDRESS A large database comprising • the catches self-collected by the fishermen of a selected list of fishing vessels was processed. This database was kindly provided by the CNR IAMC as partner within the MANTIS Project. The dataset of the red mullet • fishery from the Strait of Sicily is stored as a data frame with the following fields: UTC, Length, Num of Fishing Ground, Year, Month. There are 22556 observations, one for each sampled fish.
WEB ADDRESS Analysis of database of catches for the Red mullet in the Strait of Sicily The length-frequency distribution of the population spans within a minimum • of 6 cm to a maximum of 26 cm, the mean value is about 17 cm. The sampling time-span ranges from the 2009 to the 2015, for a total of 7 • years of data. The sampling points positions are collected from 19 of the 50 fishing ground • considered in this case study.
WEB ADDRESS Analysis of database of catches for the Red mullet in the Strait of Sicily In order to allocate fish in the different cohorts, we assume a Normal • mixture model in which the mean of the components (the cohorts) is the von Bertalanffy growth function. At age t, the expected length of a fish is given by: L t =L ∞ e−k(t−t 0 ) (von Bertalanffy) or L t =ae −be* exp(-ct) (Gompertz function) As we cannot observe the age of fish, we assume a mixture model for the • lengths. The model has been estimated under a Bayesian perspective, using the • software JAGS (Just Another Gibbs Sampler: It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation)
WEB ADDRESS The growth model The software JAGS offers several advantages. In particular, it has a cross- • platform engine and it is designed to work closely with the R language and environment. In fact, the routine has been integrated in R using the rjags package. The model requires only the specification of the model, up to the prior • distributions, and the data. It crates a posterior sampler, runs a Markov chain, and returns several descriptive statistics. As output, the model returns the estimated age of each individual in the • catches
WEB ADDRESS The growth model
WEB ADDRESS The growth model A key output provided by the • growth model is represented by the Age-Length key Obviously it also provides the • characteristics of each cohort/year in terms of mean length and variance
WEB ADDRESS The growth model Moreover, it allows estimating the trends of catches, survivors and mortality • for each cohort/year
WEB ADDRESS The growth model Last but not least, the spatial distribution of each cohort/year could be • explored Age 0 – “
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